3 Course structure The course has two parts:Lectures - Introduction to the main topicsTwo projects (done in groups)1 programming project.1 research project.Lecture slides are available on the course web page.CS583, Bing Liu, UIC

8 Feedback and suggestionsYour feedback and suggestions are most welcome!I need it to adapt the course to your needs.Let me know if you find any errors in the textbook.Share your questions and concerns with the class – very likely others may have the same.No pain no gainThe more you put in, the more you getYour grades are proportional to your efforts.CS583, Bing Liu, UIC

9 Rules and PoliciesStatute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned.Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheating will be to receive a 0 for the item in question, and dropping your final course grade one letter. The MAXIMUM penalty will be expulsion from the University.Late assignments: Late assignments will not, in general, be accepted. They will never be accepted if the student has not made special arrangements with me at least one day before the assignment is due. If a late assignment is accepted it is subject to a reduction in score as a late penalty.CS583, Bing Liu, UIC

12 Classic data mining tasksClassification:mining patterns that can classify future (new) data into known classes.Association rule miningmining any rule of the form X  Y, where X and Y are sets of data items. E.g.,Cheese, Milk Bread [sup =5%, confid=80%]Clusteringidentifying a set of similarity groups in the dataCS583, Bing Liu, UIC

13 Classic data mining tasks (contd)Sequential pattern mining:A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidenceDeviation detection:discovering the most significant changes in dataData visualization: using graphical methods to show patterns in data.CS583, Bing Liu, UIC

14 Why is data mining important?Computerization of businesses produce huge amount of dataHow to make best use of data?Knowledge discovered from data can be used for competitive advantage.Online e-businesses are generate even larger data setsOnline retailers (e.g., amazon.com) are largely driving by data mining.Web search engines are information retrieval (text mining) and data mining companiesCS583, Bing Liu, UIC

15 Why is data mining necessary?Make use of your data assetsThere is a big gap from stored data to knowledge; and the transition won’t occur automatically.Many interesting things that one wants to find cannot be found using database queries“find people likely to buy my products”“Who are likely to respond to my promotion”“Which movies should be recommended to each customer?”CS583, Bing Liu, UIC

16 Why data mining? The data is abundant.The computing power is not an issue.Data mining tools are availableThe competitive pressure is very strong.Almost every company is doing (or has to do) itCS583, Bing Liu, UIC

20 Text mining Data mining on text Main topicsDue to online texts on the Web and other sourcesText contains a huge amount of information of almost any imaginable type!A major direction and tremendous opportunity!Main topicsText classification and clusteringInformation retrievalInformation extractionOpinion miningCS583, Bing Liu, UIC